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license: mit
task_categories:
- tabular-classification
- biology
tags:
- single-cell
- scRNA-seq
- deep-learning
pretty_name: scLightGAT Data
---
# scLightGAT Data
This repository contains the training and testing datasets for **scLightGAT**: A range-constrained Graph Attention Network for single-cell clustering and annotation.
These files are structured to be compatible with the [scLightGAT project](https://github.com/TBD).
## Dataset Structure
The dataset contains processed `.h5ad` files organized for the scLightGAT pipelines.
- **`Integrated_training/`**: Contains `train.h5ad`, the large-scale reference training set used for the DVAE and GAT models.
- **`Independent_testing/`**: Contains independent datasets used for benchmarking and inference (e.g., `sapiens_full`, `lung_full`, `GSE115978`, etc.).
- **`caf.data/`**: Additional data specific to Cancer-Associated Fibroblasts (CAF) experiments.
### Directory Layout
When downloaded, the data should follow this structure to work with `run_sclight.gat.sh`:
```
scLightGAT_data/
├── Integrated_training/
│ └── train.h5ad
├── Independent_testing/
│ ├── GSE115978.h5ad
│ ├── GSE123139.h5ad
│ ├── GSE153935.h5ad
│ ├── GSE166555.h5ad
│ ├── Zhengsorted.h5ad
│ ├── lung_full.h5ad
│ └── sapiens_full.h5ad
└── caf.data/
├── caf_train.h5ad
└── caf_test.h5ad
```
## How to Use
### 1. Automated Download (Recommended)
You can use the `download_hf_data.sh` script provided in the scLightGAT repository to automatically fetch and place this data.
### 2. Manual Download
If you are manually setting up the project, download all files from this repository and place them in a directory named `scLightGAT_data` inside your project's `data/` folder.
**Project Structure Example:**
```
scLightGAT_Project/
├── scLightGAT.main/ # Code repository
│ ├── run_sclight.gat.sh
│ └── ...
└── data/
└── scLightGAT_data/ # This dataset (Downloaded here)
├── Integrated_training/
├── Independent_testing/
└── caf.data/
```
### Python Access
You can also access the files directly via `huggingface_hub`:
```python
from huggingface_hub import hf_hub_download
import scanpy as sc
# Example: Load the training data
file_path = hf_hub_download(
repo_id="Alfiechuang/scLightGAT",
filename="Integrated_training/train.h5ad",
repo_type="dataset"
)
adata = sc.read_h5ad(file_path)
```
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